API models · Side by side

Claude Sonnet 5.5 vs Claude Opus 5.5

Two Claude models with the same supported input types and context allowance, but different usage costs. The decision is whether your particular work benefits enough from Opus to justify the extra spend.

The short answer

Use the same request size to compare the bill first. Both accept text, images and files, so basic input compatibility does not separate them. Output length and thinking settings can still change what an actual task costs.

Compare the details

Compare context, supported inputs and usage costs. Highlighted rows show a difference.

Claude Sonnet 5.5 vs Claude Opus 5.5 specifications
FeatureClaude Sonnet 5.5Claude Opus 5.5
Input formatsfile, image, textfile, image, text
Output formatstexttext
Context / position limit1,000,0001,000,000
Maximum output tokens128,000128,000
Developer featuresReasoning output, Completion limit, Output limit, Reasoning controls, Thinking effort, Response format, Stop sequences, Structured output, Tool selection, Tool calling, Response lengthReasoning output, Completion limit, Output limit, Reasoning controls, Thinking effort, Response format, Stop sequences, Structured output, Tool selection, Tool calling, Response length
Cached input / 1M tokens$0.10$0.20
Cache write / 1M tokens$2.50$5.00
USD input / 1M tokens$2.00$4.00
USD output / 1M tokens$10.00$20.00
Model identifieranthropic/claude-sonnet-5.5anthropic/claude-opus-5.5

API pricing

Prices are in USD per million tokens. Input is what you send; output is what the model generates. Claude and ChatGPT subscriptions are billed separately.

Tools, images and additional reasoning can add charges. The examples below cover input and output tokens only.

Compare local models

What those prices mean in practice

Each example uses the same token budget for both models. These are calculations, not measured workloads. Cache discounts, media, tools and extra reasoning are excluded.

Token cost examples
WorkloadClaude Sonnet 5.5Claude Opus 5.5
1,000 short requestsPer request: 2,000 input + 500 output tokens$9.00$18.00
100 document summariesPer request: 20,000 input + 1,000 output tokens$5.00$10.00
10 long-document requestsPer request: 300,000 input + 2,000 output tokens$6.20$12.40

Estimate = requests × (input tokens × input price + output tokens × output price) ÷ 1,000,000. Long-prompt rates are applied where the pricing data provides them. Real requests can use different amounts of output.

Beyond the price tag

One workflow, two models

A team can keep ordinary drafting and clearly scoped changes on one model while reserving another for selected tasks. That is a routing decision your application makes, not a feature automatically included by choosing Claude. Keep the same documents, instructions and output requirements when deciding whether a switch is useful.

Budget the response

A long answer can cost more than the material sent to the model. For reports and generated code, compare the output rate as well as input. Set an output limit that allows the task to finish, and count any billable reasoning rather than budgeting only the text visible in the final answer.

Moving an existing integration

Both are Anthropic models, but keeping the same vendor does not remove the need to review responses. Tool arguments, JSON fields and the style of generated content can affect downstream behavior. Version and model IDs should be explicit in your configuration so a later update does not quietly change a production workflow.

Common questions

Are these monthly subscription prices?

No. The table compares token charges for using these models in an application. Consumer subscriptions have their own prices and usage rules.

Do the cost examples include everything?

They include input and output token charges under the stated assumptions. Media, tools, additional reasoning and cache operations can add different charges.